[11] Rich Caruana. Multitask learning. Machine learning, 28:41-75, 1997. 2
[12] Nicolò Cesa-Bianchi, Pierre Laforgue, Andrea Paudice, et al. Multitask online mirror descent. Transactions of Machine Learning Research. 2, 3, 5, 8
[13] Yuxin Chen, Adish Singla, Oisin Mac Aodha, Pietro Perona, and Yisong Yue. Understanding the role of adaptivity in machine teaching: The case of version space learners. In NeurIPS, 2018. 3
[14] Carlo Ciliberto, Youssef Mroueh, Tomaso Poggio, and Lorenzo Rosasco. Convex learning of multiple tasks and their structure. In ICML, 2015. 2, 3
[15] Sebastian Claici, Edward Chien, and Justin Solomon. Stochastic Wasserstein barycenters. In ICML, 2018. 23
[16] Rodney Coleman. Calculus on normed vector spaces. Springer Science & Business Media, 2012. 3, 6, 16
[17] Katherine M Collins, Umang Bhatt, Weiyang Liu, Vihari Piratla, Ilia Sucholutsky, Bradley Love, and Adrian Weller. Human-in-the-loop mixup. In UAI, 2023. 10
[18] Thomas H Cormen, Charles E Leiserson, Ronald L Rivest, and Clifford Stein. Introduction to algorithms. MIT press, 2022. 4
[19] Francesco Dinuzzo, Cheng S Ong, Gianluigi Pillonetto, and Peter V Gehler. Learning output kernels with block coordinate descent. In ICML, 2011. 3
[20] Emilien Dupont, Hyunjik Kim, SM Ali Eslami, Danilo Jimenez Rezende, and Dan Rosenbaum. From data to functa: Your data point is a function and you can treat it like one. In ICML, 2022. 2
[21] Theodoros Evgeniou, Charles A Micchelli, Massimiliano Pontil, and John Shawe-Taylor. Learning multiple tasks with kernel methods. Journal of machine learning research, 6(4), 2005. 2, 3, 8
[22] Jerome H Friedman. Greedy function approximation: a gradient boosting machine. Annals of statistics, pages 1189-1232, 2001. 3
[23] Peter Gehler and Sebastian Nowozin. On feature combination for multiclass object classification. In ICCV, 2009. 2, 8
[24] Izrail Moiseevitch Gelfand, Richard A Silverman, et al. Calculus of variations. Courier Corporation, 2000. 5, 16
[25] Moritz Hardt, Ben Recht, and Yoram Singer. Train faster, generalize better: Stability of stochastic gradient descent. In ICML, 2016. 4
[26] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016. 3
[27] Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Manuel Gomez Rodriguez, Andreas Krause, Pietro Perona, Yisong Yue, and Adish Singla. Teaching multiple concepts to a forgetful learner. arXiv preprint arXiv:1805.08322, 2018. 3
[28] Sham M Kakade, Shai Shalev-Shwartz, and Ambuj Tewari. Regularization techniques for learning with matrices. The Journal of Machine Learning Research, 13(1):1865-1890, 2012. 3, 5
[29] Parameswaran Kamalaruban, Rati Devidze, Volkan Cevher, and Adish Singla. Interactive teaching algorithms for inverse reinforcement learning. arXiv preprint arXiv:1905.11867, 2019. 3
[30] Akash Kumar, Hanqi Zhang, Adish Singla, and Yuxin Chen. The teaching dimension of kernel perceptron. In AISTATS, 2021. 3
[31] Peter D Lax. Functional analysis, volume 55. John Wiley & Sons, 2002. 4
[32] Laurent Lessard, Xuezhou Zhang, and Xiaojin Zhu. An optimal control approach to sequential machine teaching. In AISTATS, 2019. 3
[33] Ji Liu, Xiaojin Zhu, and Hrag Ohannessian. The teaching dimension of linear learners. In ICML, 2016. 1, 3
[34] Qiang Liu. Stein variational gradient descent as gradient flow. In NeurIPS, 2017. 3
[35] Qiang Liu and Dilin Wang. Stein variational gradient descent: A general purpose bayesian inference algorithm. In NeurIPS, 2016. 3